A Strong Generalized Fault Diagnosis Method for PMSM Drives With Image Fusion and Transfer Learning
The dead zone effect of the inverter,the nonlinear characteristics,the parameter mismatch,sampling deviation,and the setting error of the controller will lead to the three-phase current of the permanent magnet synchronous motor(PMSM)containing high-order harmonics,and then result in torque fluctuation and vibration noise.This paper proposes a data-driven diagnosis method based on image fusion and deep learning to solve this problem.First,a multi-source fault database is established based on simulation and experimental data.The three-phase current signal of a PMSM is used as the original data source without the aid of an external testing instrument.The spectrum image is obtained by a short-time Fourier transform.The time-frequency gray images are fused into a color image by the method of image fusion.After classifying and labeling the data,the samples are trained using the transfer learning of SqueezeNet.The test results show that the fault diagnosis accuracy of this method is 98.63%.Compared with the traditional method,the proposed method realizes the system-level multi-source fault diagnosis and has higher practicability.Moreover,the feasibility and generalization of fault diagnosis under the condition of insufficient sample data are realized,and the accuracy of fault diagnosis is effectively improved.
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